Pooled Mean Group Estimation of the Bilateral Trade Balance Equation: US vis-à-vis her Trading Partners

Transcription

1 Pooled Mean Group Estimation of the Bilateral Trade Balance Equation: US vis-à-vis her Trading Partners Gour Gobinda Goswami Department of Economics North South Universy 12 Kemal Ataturk Avenue Banani, Dhaka 1213 Bangladesh and Sadaquat H. Junayed Department of Economics 1125 Colonel By Drive Carleton Universy Ottawa, ON K1S 5B6 Canada Abstract Autoregressive Distributed Lag Model (ARDL) even though distinguishes between the short run and the long run effect allows both the intercepts and slopes to vary across countries. On the other hand, the static panel estimation such as fixed-effects estimation (FE) cannot distinguish between the short run and the long run behavior. To address the issue of short run heterogeney as well as long run homogeney of the estimated coefficients in a panel framework the pooled mean group (PMG) estimator (Pesaran, Shin, & Smh, 1999) has gained populary in recent days. In this paper, we estimate the bilateral trade balance model for the US vis-à-vis her nineteen OECD trading partners for the period 1973q1-2004q4 using PMG estimator and find that PMG performs better than ARDL, FE, and MG estimators and provides significant and theoretically consistent result. JEL Classification: F31 Keywords: Bilateral Trade Balance Equation; Pooled Mean Group Estimator; Panel Data. Corresponding author. gour@northsouth.edu; Tel: to 20 Ext. 138; Fax:

2 1. Introduction A major cricism against the aggregate approach in trade balance equation estimation is that suffers from aggregation-bias. A currency may appreciate wh respect to one currency and depreciate wh respect to other partner currencies. 1 To address this problem, the bilateral approach has received considerable attention among researchers in recent years (Rose and Yellen, 1989; Marquez, 1990; Marwah and Klein, 1996; Bahmani-Oskooee & Brooks, 1999; Bahmani-Oskooee & Ratha, 2003a; Bahmani- Oskooee & Ratha, 2003b; Bahmani-Oskooee & Goswami, 2004). The main body of lerature on the bilateral approach uses group-specific estimation or country-by-country single equation estimation. Individual country estimation often uses too few observations and may also suffer from omted variable bias and functional form misspecification. Consequences may be unusual sign and insignificant coefficients of the bilateral trade balance equation. The insufficient number of observations per group intensifies the problem since too many predictor variables, their differences, and lags lead to excessive loss of degrees of freedom. One prospective solution would be to address this issue by pooling data across time for all the trading countries. Tradional pooled OLS estimation restricts all intercepts and slopes to be the same across countries. The fixed-effects (henceforth, FE) and the random-effects estimation (henceforth, RE) allow intercept heterogeney across countries while assuming slopes to be the same. No distinction is made between the short run and the 1 The papers that follow the aggregate approach are Kreinin (1967), Houthakker & Magee (1969), Warner & Kreinin (1983), Bahmani-Oskooee (1985, 1986, 1991), Rosenweig & Koch (1988), Himarios (1989), Bahmani-Oskooee & Malixi (1992), Backus et al. (1994). 1

3 long run eher in the FE or the RE estimation. On the other hand, the Autoregressive Distributed Lag (henceforth, ARDL) approach (Pesaran, Shin, & Smh, 2001), makes a distinction between the short run and the long run but cannot resolve the problem of eher short run heterogeney or long run convergence. The ARDL-based empirical lerature mainly estimates similar models wh different time ranges, data frequencies, and/ or different combinations of countries or geographic regions. Simply adding more countries, or observations may not add adequate information to the estimation process. The ARDL approach, allowing long run and short run coefficients to differ across groups, may be considered as too general. Due to globalization, increasing regional cooperation, trade, and technology transfer, most of the OECD countries have a tendency to converge in the long run. However, idiosyncratic factors create short run heterogeney among the trading partners. A potentially better way to deal wh this suation is to employ a panel framework and allow short run heterogeney in conjunction wh long run homogeney of the relative price, and income coefficients in the bilateral trade balance equation. To address these issues we use the Pooled Mean Group estimator (henceforth, PMG) in estimating the bilateral trade balance equations for the US vis-à-vis her nineteen OECD trading partners over the period 1973q1-2004q4. Pooling data can provide more variabily, less collineary, more degrees of freedom, and more efficiency. 2 PMG is an intermediate procedure that constrains the long run coefficients to be the same but the short run coefficients and error variances can differ across groups (Pesaran et al., 1999). Section 2 provides an empirically testable bilateral trade balance model, while Section 3 2 For details about the gains from using panel data see Baltagi (1995), pp

4 provides information about data and the variables, Section 4 explains the regression results, Section 5 provides some sensivy analysis, and lastly Section 6 concludes. 2. The Bilateral Trade Balance Model We use the standard form of the bilateral trade balance equation in line wh Bahmani-Oskooee & Brooks (1999) 3 The estimation follows the PMG estimation framework introduced by Pesaran et al. (1999), henceforth PSS. The panel version of the model in an ARDL (p, q, q,, q) FE framework takes the following form: lntb + q j= 0 δ = α 2ij i + lnyp - j p j= 1 + λ ij q j= 0 lntb δ 3ij - j + q j= 0 lnrex δ lnyd - j 1ij + ε - j [1] VX where i=1, 2 19, t=1973, ln TB = ln represents the bilateral trade VM VX VM balance where and are the bilateral value of exports and the value of imports respectively; lnyd represents domestic country s income; ln YP represents partner country s income; P ln REX = ln p * EX P d stands for the bilateral real exchange rate 3 Bahmani-Oskooee & Brooks (1999), use ARDL approach to explore the J-Curve effect by using the bilateral trade balance equation while this study introduces panel approach in a different perspective. Studies like Habermeier & Mesqua (1999), Freeman (2000), Bassanini & Scarpetta (2002), and Slok (2002) use PMG approach in other applications but nobody uses PMG in the context of bilateral trade balance equation so far. 3

5 P p where represents partner country s price; P represents domestic country s price; d and EX represents bilateral nominal exchange rate expressed as the price of partner country s currency in terms of domestic currencies. Hence, any increase in REX represents a real depreciation of domestic currency. The expected sign of the parameters of equation [1] are relevant at this stage. It is expected that δ < 0 1 which state that the trade balance is expected to decrease as a result of an increase in domestic income in that leads to an increase in imports. The coefficient δ 2 > 0 states that increase in foreign income leads to a boost in domestic export and the trade balance is expected to improve. The coefficient δ > 0 3 states that a real depreciation of domestic currency leads to an improvement in the trade balance ceteris paribus. Following PSS, the re-parameterized form of equation 1 can be formulated as follows: lntb + q 1 j = 0 = α + φ lntb * δ lnyd 1ij i i -j + q 1 j= β lnyd * δ lnyp 2ij 1i -j + + β lnyp q 1 j= 0 2i * δ lnrex 3ij + β lnrex 3i -j + ε + p 1 j= 1 λ * ij lntb [2] -j However, PSS use an alternative approach to estimate the short run and the long run parameters. They follow ML approach by assuming that ε are normally distributed. First of all, they prepare the likelihood of panel data wrten as the product of the likelihoods for each group. Then ML estimation of the long run coefficients and the group-specific 4

6 error-correction coefficients are estimated by maximizing the concentrated log likelihood function. This is known as the PMG estimation developed by PSS. The PMG estimators are computed by back-substution algorhm that sets the first derivative of the concentrated log likelihood function equal to zero in finding out the optimal solutions. 4 We use the GAUSS program provided by PSS after making necessary adjustments according to the requirement of our bilateral trade balance model. 5 Once the long run parameters are estimated by using pooled ML estimation, the short run coefficients can consistently be estimated by running the individual OLS regressions of lntb. The lag length is selected based on the Akaike Information Creria (henceforth, AIC). Following PSS, the maximum number of lags for each variable is set at three. The MG estimators are computed by taking the simple average of the error correction coefficients and the other short run parameters. Before presenting the estimation results let us discuss the variables and the data. 3. Description of Data and the Variables We use quarterly data from nineteen OECD trading partners of the US for the period 1973q1-2004q4. 6 The original source of data for trade balance is the Direction of Trade Statistics of the IMF. The trade balance is constructed from the data on bilateral value of exports and the value of imports and the specific procedure has already been discussed in the earlier section. We collect the data for bilateral trade flows from 1973q1-4 Inial estimates that are used in the eration procedure for the long run parameters of the pooled ML estimation are obtained from the MG estimation in our case. 5 To download the original GAUSS program developed by PSS vis Pesaran s webpage at 6 The list includes Australia, Austria, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Korea, Netherlands, Norway, Portugal, Spain, Sweden, Swzerland, and the UK. The US is the domestic country in our model. 5

7 1997q4 from the IMF tape available in ICPSR database of the Universy of Michigan, Ann Arbor (Study No. 7628). 7 The remaining data of bilateral trade flow for 1998q1-2004q4 are collected from the paper version of the Direction of Trade Statistics of the IMF. Data are verified to check for consistency between the two sources. Industrial production index is used as a proxy for the domestic and the partner country s income for all countries except Greece. For Greece, manufacturing production index is used. The data on industrial production index and manufacturing production index are taken from the IFS online CD-ROM. The data on period-average bilateral nominal exchange rate is taken from National Accounts, OECD. The OECD data set converts all the previous currencies of the European countries into their EURO equivalents (where necessary). Consumer s Price Index (CPI) is used as a proxy for domestic and foreign prices. The data on CPI is taken from the IFS online CD-ROM. In the next section we report the estimation result of equation [2]. 4. Regression Results We begin wh reporting the ARDL and the PMG results for the short-run coefficients of the equation [2] in Table 1. <Table 1 here> 7 For detailed explanation of the ICPSR data vis the webse 6

8 In both Panel A and Panel B we observe that the speed of adjustment ( ) gets smaller in absolute values as we swch from the ARDL to the PMG in most of the cases. It appears that the ARDL approach overestimates the adjustment parameter and the PMG introduces country-specific factors into the estimation and thus yielding slower speed in the short run. However, the significance of ( φ i ) remains intact in most of the cases both in the ARDL and the PMG estimation. The insignificance and wrong sign of the short run coefficients of lnyp for some countries in ARDL estimation turns out to be significant and takes correct sign when we estimate the PMG instead. The negative signs of lnrex t, lnrex t-1, followed by posive sign of lnrex t-2 for Finland and the negative coefficients for France, and Swzerland is an indication of the J-Curve effect for these countries in a limed sense if the long run coefficients are found as posive. 8 To explore the long run coefficients of the ARDL procedure we report the ARDL estimation for individual countries in Table 2. φ i <Table 2 here> From Table 2 we find that the coefficient of lnyd in the case of Denmark, Finland, Ireland, Italy, Japan, and Korea, the coefficient of lnyp in the case of Austria, Denmark, Finland, Greece, Ireland, Italy, Japan, Korea, Portugal, Spain, and Swzerland, and the coefficient of lnrex in the case of Norway all take wrong sign when we use the ARDL approach. In many of the countries, the estimated coefficients are insignificant 8 It is not possible to test the J-Curve effect by using the PMG estimator for all the countries because the estimation procedure does not allow us to include enough lags in the panel. Our program allows maximum three lags for AIC and four lags for SBC only to save some degrees of freedom. However, the addional gain in PMG outweighs the loss incurred by using the ARDL in that the PMG allows for individual country heterogeney. 7

9 even though they take the correct sign. The negative coefficients of the real exchange rate (lnrex) in the short run but posive and significant long run coefficient for Finland, France, and Swzerland support the J-Curve hypothesis. It is now sensible to explore if there is any gain if we use PMG instead of ARDL, FE, or MG approach. We report the panel estimation results in Table 3. <Table 3 goes here> Table 3 reports panel estimation results from the FE, MG, and PMG estimator. As can be noticed from Table 3, two of the three panel estimations provide theoretically consistent signs of all the three coefficients. The coefficient of lnyp takes wrong sign in FE estimation whereas the same coefficient turns out to be posive but insignificant when we use MG estimation. The insignificant coefficient of lnyp in the MG estimation turns out to be significant in the PMG estimation. Hence, as far as the sign, significance, and the theoretical consistency of the estimated coefficients are concerned the PMG performs the best among all the three panel estimations. The bilateral real exchange rate coefficient in the PMG estimation is found to be greater than one and significant. This postulates that price policy, and exchange rate policy plays important roles in stabilizing the external sector of the US vis-à-vis OECD economies in the long run. The price elasticy is found to be This result is a major improvement in that the coefficient of lnrex increases from 0.39 to 1.24 when we swch from the FE to the PMG estimation. 9 The impact of a real depreciation on trade balance is more than proportional in the PMG while is less than proportional in the FE 9 This also supports that Marshal-Lerner condion holds in OECD trade. 8

10 estimation. Economic size matters for trade balance of the US vis-à-vis her OECD partners because the estimated coefficients of lnyd and lnyp are found to be significant when we use the PMG estimation. The Likelihood Ratio statistic for testing the equaly of long run parameters is This is distributed as Chi-Squared wh 54 degree of freedom and s p-value is The estimated joint Hausman test statistic (h-test) is 4.45 wh s associated p- value equal to Hence, the null hypothesis of homogeney of slopes in the long run is not rejected for all variables jointly. Similar results are found when we observe individual Hausman test statistic for each variable separately in Table 3. This is also evidence against the appropriateness of country-by-country ARDL estimation. Lower absolute values of the error correction coefficients ( ) in the PMG compared to the MG represent slower speed of adjustment of any deviation from the steady state as a result of any shock in the process. 10 This states that individual countryspecific factors might have resulted in slower speed of adjustment in the US vis-à-vis her φ i OECD partner countries. 11 However, the negative and significant φ represents the presence of cointegration among variables in each case no matter whether we use the ARDL or the PMG approach. Another indication of this result is that the US being a defic country has bilateral sustainabily in her trade balance wh the OECD countries in the long run. This inherent sustainabily cannot be revealed if we just use ARDL, MG, or FE estimator. i φ i 10 The estimated is found to be wh t-ratio of in the case of MG whereas is wh a t-ratio of in the case of PMG. 11 PMG allows for both intercept and slope heterogeney in the short run keeping the long run homogeney assumption valid. Hence, PMG is much more flexible compared to pooled-ols, FE, and MG estimators. 9

11 5. Sensivy Analysis As a sensivy test we estimate the same model wh different maximum lag length such as three lags and four lags wh the Schwarz-Bayesian Crerion (henceforth, SBC) and find that the results are robust in the sense that the sign and the significance of the variables do not change. Hence, our result holds no matter whether we use the AIC or the SBC. We also use Newton-Raphson algorhm, which uses both the first and the second derivative of the concentrated log likelihood function and find that the result does not change much. As an addional robustness check we introduce a trend term in the model to control for deterministic trend (if there is any) and find that the result mostly supports for the PMG. Interestingly, the trend term is found as significant. To check if there is any impact of outlying countries we drop Greece, and Korea from the dataset and run the same model wh only seventeen trading partners of the US. It is found that the performance of PMG still remains intact. We also check wh using the estimated FE coefficients instead of the coefficients of the MG estimation as the inial values during the eration procedure and find that PMG still performs the best. One thing that we would like to mention is that using different algorhms, information creria, and trend term sometimes distort the result of FE, or MG estimator but PMG coefficients are robust to all the suations Conclusion Bilateral approach in estimating the trade balance equation has gained momentum in recent years to address the problem of aggregation bias in earlier studies. In bilateral approach, researchers mostly use the ARDL approach. One potential problem of the 12 Data, GAUSS program, and all the sensivy test results are available from the author upon request. 10

12 ARDL approach is that the sign and the significance of the parameters are too much sensive to the selection of partners, and time periods. Due to insufficient information about each country, increasing the number of partner countries does not solve the problem of omted variables, misspecification, and multicollineary among the right hand side variables. As a result, estimation of individual country equation by the ARDL approach may not provide us wh precise estimates of all the short run and the long run parameters including the speed of adjustment coefficient. The estimated coefficients in many cases produce counterintuive sign and insignificant coefficients for many countries. In addion to this, in ARDL approach, all the short run and long run coefficients are assumed to be different across countries and thus may not reflect the suation of recent globalization and increasing cooperation among trading countries. To address this issue, we use quarterly data for nineteen OECD trading partners of the US over the period 1973q1-2004q4 and use PMG estimator developed by PSS that assumes long run homogeney and allowing for short run heterogeney. We check the sensivy of our result by allowing maximum three lags or four lags and use the SBC and find that there is no significant change in the result and the PMG estimation performs the best. Using different algorhms in ML procedure and the introduction of trend term also do not cause noteworthy change in result. This is the first panel study in estimating the bilateral trade balance equation using the PMG approach to resolve many of the potential problems of the group-specific ARDL estimation as well as the tradional MG or FE estimation. We find that the results from the PMG estimator are more theoretically 11

13 consistent and free from many limations that are contained in the ARDL, FE, or MG estimators. Acknowledgement We would like to acknowledge the valuable suggestions for improvement from an anonymous referee and the edor of this journal, Professor Malcolm Sawyer. Our special thanks go to Professor Y. Shin regarding the degree of freedom issue in the lag selection procedure and Professor M. Bahmani-Oskooee for overall suggestion in an earlier draft of this paper. However, any errors are authors. References Backus, D. K., Kehoe, P. J. & Kydland, F. E. (1994) Dynamics of the trade balance and the terms of trade: the J-Curve? The American Economic Review, 84(1), pp Bahmani-Oskooee, M. (1985) Devaluation and the J-Curve: some evidence from LDCs, The Review of Economics and Statistics, 67, pp Bahmani-Oskooee, M. (1986) Determinants of international trade flows: the case of developing countries, Journal of Development Economics, 20, pp Bahmani-Oskooee, M. (1991) Is there a long run relation between the trade balance and the real effective exchange rate of LDCs?, Economics Letters, 36, pp Bahmani-Oskooee, M. & Brooks, T. J. (1999) Bilateral J-Curve between US and her trading partners, Weltwirtschaftliches Archiv, 135, pp Bahmani-Oskooee, M. & Goswami, G. G. (2004) Exchange rate sensivy of Japan s bilateral trade flows, Japan and the World Economy, 16, pp

14 Bahmani-Oskooee, M. & Malixi, M. (1992) More evidence on the J-Curve from LDCs, Journal of Policy Modeling, 14, pp Bahmani-Oskooee, M. & Ratha, A. (2003a) Dynamics of the US trade wh developing countries, Journal of Developing Areas, forthcoming. Bahmani-Oskooee, M. & Ratha, A. (2003b) The J-Curve dynamics of US bilateral trade, Journal of Economics and Finance, forthcoming. Baltagi, B. (1995) Econometric Analysis of Panel Data, New York: Wiley. Bassanini, S. & Scarpetta, S. (2002) Does human capal matter for growth in OECD countries? A pooled mean-group approach, Economics Letters, 74, pp Freeman, D. (2000) Alternative panel estimates of alcohol demand, taxation, and the business cycle, Southern Economic Journal, 67(2), pp Habermeier, K. & Mesqua, M. (1999) Long-run exchange rate dynamics: a panel data study, Working Paper of the International Monetary Fund, WP/99/50. Houthakker, H. S. & Magee, T. P. (1969) Income and price elasticies in world trade, The Review of Economics and Statistics, 51, pp International Monetary Fund, (1999) Direction of Trade [Computer file]. ICPSR version. Washington DC: International Monetary Fund [producer]. Ann Arbor, MI: Interuniversy Consortium for Polical and Social Research [distributor] International Monetary Fund, Direction of Trade Statistics, Various Issues. International Monetary Fund, IFS Online CD-ROM. Kreinin, M. E. (1967) Price elasticies in the international trade, The Review of Economics and Statistics, 49, pp

15 Marquez, J. (1990) Bilateral trade elasticies, The Review of Economics and Statistics, 75, pp Organization for Economic Cooperation and Development, National Accounts, CD-ROM. Pesaran, M. H., Shin, Y. & Smh, R. J. (1999) Pooled mean group estimation of dynamic heterogeneous panels, Journal of the American Statistical Association, 94, pp Pesaran, M. H., Shin, Y. & Smh, R. J. (2001) Bounds testing approach to the analysis of level relationship, Journal of Applied Econometrics, 16, pp Rosenweig, J. A. & Koch., P. D. (1988) The US dollar and the delayed J-Curve, Economic Review, Federal Reserve Bank of Atlanta, 73, pp Slok, T. (2002) Money demand in Mongolia: a panel data analysis, IMF Staff Papers, 49(1), pp Warner, D. & Kreinin, M. E. (1983) Determinants of international trade flows, The Review of Economics and Statistics, 65, pp

16 Table 1: Short Run Coefficients of the Pooled Mean Group vs. Group-Specific ARDL Estimates Based on the Akaike Information Creria Panel A Group Constant Error Correction Coefficient lnyd lnyp lnrex lntb t-1 lntb t-2 lnyd t ( φi ) Australia PMG (2.33) (2.51) (2.43) 0.01 (1.69) 0.09 (2.49) (3.47) (2.49) 1.87 (2.55) Australia ARDL (2.07) (3.63) (1.34) 0.57 (1.93) 0.20 (1.76) (2.02) (1.66) 1.69 (2.31) Austria PMG (3.63) (4.14) (3.72) 0.05 (2.00) 0.30 (3.98) (2.61) n. a. n. a. Austria ARDL (1.66) (3.39) (0.12) (0.09) 0.32 (2.36) (2.40) n. a. n. a. Canada PMG (1.57) (1.61) (1.59) 0.01 (1.31) 0.03 (1.61) (1.45) (2.88) (2.02) Canada ARDL (1.82) (2.15) (0.12) 0.06 (0.41) 0.13 (1.84) (0.74) (2.28) (1.84) Denmark PMG (4.44) (5.28) (4.49) 0.07 (2.14) 0.45 (5.00) (2.53) (2.64) n. a. Denmark ARDL 1.88 (2.93) (4.74) 0.04 (0.14) (0.81) 0.55 (4.57) (2.30) (2.52) n. a. Finland PMG (3.62) (4.08) (3.64) 0.05 (2.00) 0.33 (3.94) (2.38) n. a (1.27) Finland ARDL (1.36) (3.70) 0.01 (0.02) (0.23) 0.30 (1.84) (2.20) n. a (1.26) France PMG (4.62) (5.03) (4.37) 0.05 (2.14) 0.34 (5.22) (3.20) (3.87) (2.81) France ARDL (1.50) (4.36) (2.26) 0.12 (0.54) 0.33 (4.48) (2.63) (3.40) (2.80) Germany PMG (5.07) (5.68) (4.72) 0.04 (2.16) 0.28 (5.78) (1.94) (4.20) (1.55) Germany ARDL (2.93) (4.45) (2.17) 0.28 (1.31) 0.28 (5.02) (1.92) (4.03) (1.26) Greece PMG (3.11) (3.85) (3.51) 0.06 (2.00) 0.39 (3.65) (1.48) (2.24) n. a. Greece ARDL (0.44) (3.85) (0.42) (0.48) 0.55 (2.32) (1.31) (2.18) n. a. Ireland PMG (0.67) (0.58) (0.58) 0.00 (0.57) 0.02 (0.58) (5.06) (2.77) n. a. Ireland ARDL (1.73) (4.02) 0.60 (1.29) (2.21) 0.27 (2.23) (4.36) (2.73) n. a. Italy PMG (3.53) (3.72) (3.31) 0.03 (1.97) 0.19 (3.68) (5.67) (2.15) (2.08) Italy ARDL (0.30) (2.62) 0.01 (0.04) (0.91) 0.27 (2.90) (4.72) (1.88) (2.09) Japan PMG (4.20) (4.38) (3.86) 0.02 (2.12) 0.12 (4.43) (2.35) (2.76) (2.31) Japan ARDL (2.66) (1.92) 0.01 (0.09) (1.53) 0.23 (4.12) (2.58) (2.90) (2.38) Korea PMG (3.73) (4.22) (3.92) 0.05 (2.16) 0.30 (4.06) (3.75) (2.75) (3.57) Korea ARDL (3.06) (3.77) 0.35 (1.31) (0.80) 0.63 (4.16) (3.55) (2.84) (3.95) Netherlands PMG (3.67) (4.68) (4.15) 0.05 (2.10) 0.32 (4.65) (2.78) (4.72) (2.44) Netherlands ARDL (0.98) (3.78) (0.56) 0.10 (0.18) 0.35 (4.06) (2.66) (4.54) (2.44) Norway PMG (2.64) (2.72) (2.58) 0.05 (1.80) 0.30 (2.66) (3.77) (2.30) n. a. Norway ARDL 2.09 (1.20) (3.34) (1.23) 0.09 (0.31) (0.26) (3.15) (2.00) n. a. Portugal PMG (1.81) (1.80) (1.76) 0.01 (1.43) 0.09 (1.79) (3.18) (4.69) n. a. Portugal ARDL 1.73 (1.57) (3.08) (1.43) (0.53) 0.17 (1.14) (2.27) (4.12) n. a. Spain PMG (3.74) (4.24) (3.67) 0.04 (2.03) 0.29 (4.23) (3.67) (4.94) (3.24) Spain ARDL (0.29) (3.80) (0.82) (0.01) 0.33 (3.53) (2.99) (4.41) (2.96) Sweden PMG (3.71) (3.93) (3.53) 0.05 (2.01) 0.32 (3.88) (3.88) (3.73) n. a. Sweden ARDL (0.94) (3.13) (1.57) 0.12 (0.35) 0.32 (3.02) (3.51) (3.52) n. a. Swzerland PMG (5.29) (6.97) (5.21) 0.10 (2.20) 0.63 (6.64) n. a. n. a (1.95) Swzerland ARDL (0.02) (7.49) (0.43) (0.59) 0.39 (2.83) n. a. n. a (1.93) UK PMG (1.91) (2.01) (1.98) 0.02 (1.53) 0.16 (1.99) (2.94) (5.00) (0.50) UK ARDL (3.18) (3.24) (3.90) 1.15 (3.73) 0.04 (0.36) (2.48) (4.68) (1.83) N. B. Figures in the parentheses represent the absolute values of the t-ratios. 15

17 Table 1: Continued.. Panel B Group lnyd t-1 lnyd t-2 lnyp t lnyp t-1 lnyp t-2 lnrex t lnrex t-1 lnrex t-2 2 Australia PMG n. a. n. a (2.55) n. a. n. a. n. a. n. a. n. a Australia ARDL n. a. n. a (2.31) n. a. n. a. n. a. n. a. n. a Austria PMG n. a. n. a. n. a. n. a. n. a (1.75) n. a. n. a Austria ARDL n. a. n. a. n. a. n. a. n. a (1.67) n. a. n. a Canada PMG n. a. n. a (2.16) n. a. n. a. n. a. n. a. n. a Canada ARDL n. a. n. a (1.91) n. a. n. a. n. a. n. a. n. a Denmark PMG n. a. n. a. n. a. n. a. n. a (2.44) n. a. n. a Denmark ARDL n. a. n. a. n. a. n. a. n. a (2.37) n. a. n. a Finland PMG (2.20) n. a (1.80) n. a. n. a (0.14) (1.84) 0.89 (2.01) 0.22 Finland ARDL (2.13) n. a (1.65) n. a. n. a (0.04) (1.66) 0.93 (1.96) 0.22 France PMG n. a. n. a. n. a. n. a. n. a (2.01) n. a. n. a France ARDL n. a. n. a. n. a. n. a. n. a (2.16) n. a. n. a Germany PMG (1.27) (2.22) 0.08 (0.15) 1.63 (3.24) n. a (3.94) (1.33) n. a Germany ARDL (1.16) (1.97) (0.02) 1.52 (2.78) n. a (3.87) (1.43) n. a Greece PMG n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Greece ARDL n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Ireland PMG n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Ireland ARDL n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Italy PMG (1.97) n. a (1.67) 2.01 (2.61) n. a. n. a. n. a. n. a Italy ARDL (1.87) n. a (1.63) 2.14 (2.60) n. a. n. a. n. a. n. a Japan PMG (0.26) (1.99) 0.76 (1.41) 0.65 (1.17) n. a (2.04) n. a. n. a Japan ARDL (0.50) (2.19) 0.79 (1.40) 0.89 (1.53) n. a (2.17) n. a. n. a Korea PMG n. a. n. a (1.89) n. a. n. a. n. a. n. a. n. a Korea ARDL n. a. n. a (2.33) n. a. n. a. n. a. n. a. n. a Netherlands PMG n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Netherlands ARDL n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Norway PMG n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Norway ARDL n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Portugal PMG n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Portugal ARDL n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Spain PMG n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Spain ARDL n. a. n. a. n. a. n. a. n. a. n. a. n. a. n. a Sweden PMG n. a. n. a. n. a. n. a. n. a (2.05) n. a. n. a Sweden ARDL n. a. n. a. n. a. n. a. n. a (1.97) n. a. n. a Swzerland PMG n. a. n. a. n. a. n. a. n. a (2.77) (1.52) (2.14) 0.24 Swzerland ARDL n. a. n. a. n. a. n. a. n. a (2.75) (1.45) (2.09) 0.29 UK PMG (0.10) n. a. n. a. n. a. n. a (2.05) n. a. n. a UK ARDL (1.39) n. a. n. a. n. a. n. a (2.33) n. a. n. a N. B. Figures in the parentheses represent the absolute values of the t-ratios. R 16

18 Table 2: Group-Specific ARDL Estimates of the Long Run Coefficients Based on the Akaike Information Creria Dependent Variable: lntb Group lnyd lnyp lnrex Australia (1.28) 2.14 (1.89) 0.75 (1.73) Austria (0.12) (0.09) 1.33 (2.10) Canada (0.12) 0.46 (0.38) 1.02 (2.01) Denmark 0.11 (0.14) (0.84) 1.49 (4.51) Finland 0.04 (0.02) (0.23) 1.12 (1.85) France (2.83) 0.37 (0.54) 1.02 (4.08) Germany (2.44) 1.21 (1.35) 1.26 (4.30) Greece (0.42) (0.49) 1.66 (2.56) Ireland 2.77 (1.48) (2.91) 1.25 (2.13) Italy 0.05 (0.04) (0.88) 1.52 (2.01) Japan 0.07 (0.09) (1.49) 2.79 (1.73) Korea 1.30 (1.40) (0.86) 2.33 (3.87) Netherlands (0.58) 0.38 (0.19) 1.39 (3.48) Norway (1.27) 0.27 (0.31) (0.25) Portugal (1.56) (0.53) 0.78 (0.99) Spain (0.82) (0.02) 1.17 (3.07) Sweden (1.52) 0.48 (0.34) 1.26 (2.71) Swzerland (0.44) (0.58) 0.63 (2.85) UK (3.08) 4.65 (2.87) 0.15 (0.38) N. B. Figures in the parentheses represent the absolute values of the t-ratios. 17

19 Table 3: Alternative Panel Estimates of the Coefficients of the Bilateral Trade Balance Equation Dependent Variable: lntb Fixed-Effects Estimator (FE) Mean Group Estimator (MG) Pooled Mean-Group Estimator (PMG) Hausman Test (h-test) P-value lnyd (12.31) (1.68) (8.19) lnyp (6.12) 0.10 (0.30) 0.19 (2.31) lnrex 0.39 (9.51) 1.20 (7.75) 1.24 (11.91) N. B. Figures in the parentheses represent the absolute values of the t-ratios. 18